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<div class="section" id="distributed-checkpoint-torch-distributed-checkpoint">
<h1>Distributed Checkpoint - torch.distributed.checkpoint<a class="headerlink" href="#distributed-checkpoint-torch-distributed-checkpoint" title="Permalink to this heading">¶</a></h1>
<p>Distributed Checkpoint (DCP) support loading and saving models from multiple ranks in parallel.
It handles load-time resharding which enables saving in one cluster topology and loading into another.</p>
<p>DCP is different than <cite>torch.save</cite> and <cite>torch.load</cite> in a few significant ways:</p>
<ul class="simple">
<li><p>It produces multiple files per checkpoint, with at least one per rank.</p></li>
<li><p>It operates in place, meaning that the model should allocate its data first and DCP uses that storage instead.</p></li>
</ul>
<p>The entrypoints to load and save a checkpoint are the following:</p>
<span class="target" id="module-torch.distributed.checkpoint"></span><dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.save">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save" title="Permalink to this definition">¶</a></dt>
<dd><p>Save a distributed model in SPMD style.</p>
<p>This function is different from <code class="docutils literal notranslate"><span class="pre">torch.save()</span></code> as it handles
<code class="docutils literal notranslate"><span class="pre">ShardedTensor</span></code> , and <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> by having each rank only save their local shards.</p>
<p>For each <code class="docutils literal notranslate"><span class="pre">Stateful</span></code> object (having both a <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and a <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code>),
save will call <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> before serialization.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>There is no guarantees of Backwards Compatibility across PyTorch versions
for saved state_dicts.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If using the <cite>process_group</cite> argument, make sure that only its ranks
call <cite>save_state_dict</cite> and that all data in state_dict belong to it.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When saving checkpoint for FSDP’s <cite>ShardingStrategy.HYBRID_SHARD</cite>, only one of
the shard_group should be calling <cite>save_state_dict</cite> and the corresponding process
group needs to be passed in.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<dl class="simple">
<dt>If no process group is available, this function assumes the intention is to save the</dt><dd><p>state_dict in the local process.</p>
</dd>
</dl>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>state_dict</strong> (<em>Dict</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>]</em>) – The state_dict to save.</p></li>
<li><p><strong>checkpoint_id</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><em>os.PathLike</em></a><em>, </em><em>None</em><em>]</em>) – The ID of this checkpoint instance. The meaning of the checkpoint_id
depends on the storage. It can be a path to a folder or to a file.
It can also be a key if the storage is a key-value store.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>storage_writer</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.StorageWriter" title="torch.distributed.checkpoint.StorageWriter"><em>StorageWriter</em></a><em>]</em>) – Instance of StorageWriter used to perform writes. If this is not
specified, DCP will automatically infer the writer based on the
checkpoint_id. If checkpoint_id is also None, an exception will
be raised. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>planner</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.SavePlanner" title="torch.distributed.checkpoint.SavePlanner"><em>SavePlanner</em></a><em>]</em>) – Instance of SavePlanner. If this is not specificed, the default
planner will be used. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>process_group</strong> (<em>Optional</em><em>[</em><em>ProcessGroup</em><em>]</em>) – ProcessGroup to be used for cross-rank synchronization.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Metadata object for the saved checkpoint.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Metadata</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">state_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"model"</span><span class="p">:</span> <span class="n">my_model</span><span class="p">}</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">fs_storage_writer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">FileSystemWriter</span><span class="p">(</span><span class="s2">"/checkpoint/1"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">save</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">state_dict</span><span class="o">=</span><span class="n">state_dict</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">storage_writer</span><span class="o">=</span><span class="n">fs_storage_writer</span><span class="p">,</span>
<span class="gp">>>> </span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>save_state_dict uses collectives to coordinate writes across ranks.
For NCCL-based process groups, internal tensor representations of
objects must be moved to the GPU device before communication takes place.
In this case, the device used is given by <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_device()</span></code>
and it is the user’s responsibility to ensure that this is set so that
each rank has an individual GPU, via <code class="docutils literal notranslate"><span class="pre">torch.cuda.set_device()</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.async_save">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">async_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#async_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.async_save" title="Permalink to this definition">¶</a></dt>
<dd><p>Asynchronous version of <code class="docutils literal notranslate"><span class="pre">save</span></code>. This code first de-stages the state_dict on to the
staging storage (defaults to CPU memory), and then calls the <cite>save</cite> in a separate thread.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This feature is experimental and subject to change.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>state_dict</strong> (<em>Dict</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>]</em>) – The state_dict to save.</p></li>
<li><p><strong>checkpoint_id</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><em>os.PathLike</em></a><em>, </em><em>None</em><em>]</em>) – The ID of this checkpoint instance. The meaning of the checkpoint_id
depends on the storage. It can be a path to a folder or to a file.
It can also be a key if the storage is a key-value store.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>storage_writer</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.StorageWriter" title="torch.distributed.checkpoint.StorageWriter"><em>StorageWriter</em></a><em>]</em>) – Instance of StorageWriter used to perform ‘stage’ and ‘save’. If
this is not specified, DCP will automatically infer the writer based on the
checkpoint_id. If checkpoint_id is also None, an exception will
be raised. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>planner</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.SavePlanner" title="torch.distributed.checkpoint.SavePlanner"><em>SavePlanner</em></a><em>]</em>) – Instance of SavePlanner. If this is not specificed, the default
planner will be used. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>process_group</strong> (<em>Optional</em><em>[</em><em>ProcessGroup</em><em>]</em>) – ProcessGroup to be used for cross-rank synchronization.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A future holding the resultant Metadata object from <cite>save</cite>.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future">Future</a></p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">state_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"model"</span><span class="p">:</span> <span class="n">my_model</span><span class="p">}</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">fs_storage_writer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">FileSystemWriter</span><span class="p">(</span><span class="s2">"/checkpoint/1"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">checkpoint_future</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">async_save</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">state_dict</span><span class="o">=</span><span class="n">state_dict</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">storage_writer</span><span class="o">=</span><span class="n">fs_storage_writer</span><span class="p">,</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="c1"># ... do some work ...</span>
<span class="go">>>></span>
<span class="gp">>>> </span><span class="n">checkpoint_future</span><span class="o">.</span><span class="n">result</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.save_state_dict">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>This method is deprecated. Please switch to ‘save’.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><em>Metadata</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_loader.load">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load a distributed <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> in SPMD style.</p>
<p>Each rank will try to read the least amount of data necessary
to fullfill the requested <cite>state_dict</cite>. When loading <code class="xref py py-class docutils literal notranslate"><span class="pre">ShardedTensor</span></code>
or <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> instances, each rank only reads data for their local shards.</p>
<p>For each <code class="docutils literal notranslate"><span class="pre">Stateful</span></code> object (having both a <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and a <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code>),
load will first call <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> before attempting deserialization, followed by
<code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> once the deserialization is complete.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>All tensors in <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> must be allocated on their
destination device <em>prior to</em> calling this function.</p>
<p>All non-tensor data is loaded using <cite>torch.load()</cite> and modified in place
on state_dict.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Users must call <cite>load_state_dict</cite> on the root module to ensure load
pos-processing and non-tensor data properly propagates.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>state_dict</strong> (<em>Dict</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><em>Any</em><em>]</em>) – The state_dict to save.</p></li>
<li><p><strong>checkpoint_id</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><em>os.PathLike</em></a><em>, </em><em>None</em><em>]</em>) – The ID of this checkpoint instance. The meaning of the checkpoint_id
depends on the storage. It can be a path to a folder or to a file.
It can also be a key if the storage is a key-value store.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>storage_reader</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.StorageReader" title="torch.distributed.checkpoint.StorageReader"><em>StorageReader</em></a><em>]</em>) – Instance of StorageWriter used to perform reads. If this is not
specified, DCP will automatically infer the reader based on the
checkpoint_id. If checkpoint_id is also None, an exception will
be raised. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>planner</strong> (<em>Optional</em><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.LoadPlanner" title="torch.distributed.checkpoint.LoadPlanner"><em>LoadPlanner</em></a><em>]</em>) – Instance of LoadPlanner. If this is not specificed, the default
planner will be used. (Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
<li><p><strong>process_group</strong> (<em>Optional</em><em>[</em><em>ProcessGroup</em><em>]</em>) – ProcessGroup to be used for cross-rank synchronization.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>None.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>None</p>
</dd>
</dl>
<dl>
<dt>Examples</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">my_model</span> <span class="o">=</span> <span class="n">MyModule</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adagrad</span><span class="p">(</span><span class="n">my_model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
<span class="gp">>>> </span><span class="n">model_state_dict</span> <span class="o">=</span> <span class="n">my_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">fs_storage_reader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">FileSystemReader</span><span class="p">(</span><span class="s2">"/checkpoint/1"</span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">checkpoint</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">state_dict</span><span class="o">=</span><span class="n">model_state_dict</span><span class="p">,</span>
<span class="gp">>>> </span> <span class="n">storage_reader</span><span class="o">=</span><span class="n">fs_storage_reader</span><span class="p">,</span>
<span class="gp">>>> </span><span class="p">)</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="c1"># module.load_state_dict() function might have customized steps</span>
<span class="gp">>>> </span><span class="c1"># to flush the state_dict, must call it to</span>
<span class="gp">>>> </span><span class="c1"># ensure correct behavior.</span>
<span class="gp">>>> </span><span class="n">my_model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">model_state_dict</span><span class="p">)</span>
</pre></div>
</div>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>load_state_dict uses collectives to coordinate reads across ranks.
For NCCL-based process groups, internal tensor representations of
objects must be moved to the GPU device before communication takes place.
In this case, the device used is given by <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_device()</span></code>
and it is the user’s responsibility to ensure that this is set so that each
rank has an individual GPU, via <code class="docutils literal notranslate"><span class="pre">torch.cuda.set_device()</span></code>.</p>
</div>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_loader.load_state_dict">
<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>This method is deprecated. Please switch to ‘load’.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
<p>The following module is also useful for additional customization of the staging mechanisms used for asynchronous checkpointing (<cite>torch.distributed.checkpoint.async_save</cite>):</p>
<span class="target" id="module-torch.distributed.checkpoint.staging"></span><dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">AsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager" title="Permalink to this definition">¶</a></dt>
<dd><p>This protocol is meant to provide customization and extensibility for dcp.async_save, allowing users
to customize how data is staged previous to executing the usual dcp.save path in parallel.
The expected order of operations (concretely defined in <cite>torch.distributed.state_dict_saver.async_save</cite>)
is the following:</p>
<ol class="arabic simple">
<li><dl class="simple">
<dt>AsyncStager.stage_data(state_dict):</dt><dd><p>This call gives the AsyncStager the opportunity to ‘stage’
the state_dict. The expectation and purpose of staging in this context is to create a “training-safe”
representation of the state dict, meaning that any updates to module data after staging is complete
should not be reflected in the state dict returned from this method. For example, in the default
case a copy of the entire state dict is created on CPU RAM and returned here, allowing users
to continue training without risking changes to data which is being serialized.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>dcp.save is called on the state_dict returned from stage in parallel. This call is responsible</dt><dd><p>for serializing the state_dict and writing it to storage.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt>If AsyncStager.should_synchronize_after_execute is True, this method will be called immediately after</dt><dd><p>the serialization thread starts and before returning from dcp.async_save. If this is set to False,
the assumption is the user has defined a custom synchronization point for the the purpose of further
optimizing save latency in the training loop (for example, by overlapping staging with the
forward/backward pass), and it is the respondsibility of the user to call <cite>AsyncStager.synchronize_staging</cite>
at the appropriate time.</p>
</dd>
</dl>
</li>
</ol>
<dl class="py property">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager.should_synchronize_after_execute">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">should_synchronize_after_execute</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></em><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.should_synchronize_after_execute" title="Permalink to this definition">¶</a></dt>
<dd><p>Whether to synchronize after executing the stage.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager.stage">
<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.stage" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a “staged” copy of <cite>state_dict</cite>. The expectation of the staged copy is that it is
innoculated from any updates incurred after the stage call is complete.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><em>Dict</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><em>Union</em></a>[<em>StatefulT</em>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a>]]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging">
<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
<dd><p>In the case <cite>stage</cite> is async in some way, this method should be called to ensure staging
is complete and it is safe to begin modifying the original <cite>state_dict</cite></p>
<dl class="field-list simple">
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">BlockingAsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager" title="Permalink to this definition">¶</a></dt>
<dd><p>An implementation of AsyncStager which stages the state_dict on CPU RAM and blocks until the copy is complete.
This implementation also provides an option to optimize stage latency using pinned memory.</p>
<p>N.B. synchronize_staging is a no-op in this case.</p>
<dl class="field-list simple">
</dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager.stage">
<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.stage" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of <cite>state_dict</cite> on the CPU.</p>
<dl class="field-list simple">
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><em>Dict</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><em>Union</em></a>[<em>StatefulT</em>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a>]]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging">
<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
<dd><p>No-op function, since staging is blocking.</p>
<dl class="field-list simple">
</dl>
</dd></dl>
</dd></dl>
<p>In addition to the above entrypoints, <cite>Stateful</cite> objects, as described below, provide additional customization during saving/loading
.. automodule:: torch.distributed.checkpoint.stateful</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.stateful.</span></span><span class="sig-name descname"><span class="pre">Stateful</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful" title="Permalink to this definition">¶</a></dt>
<dd><p>Stateful protocol for objects that can be checkpointed and restored.</p>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.load_state_dict">
<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.load_state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Restore the object’s state from the provided state_dict.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>state_dict</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Dict" title="(in Python v3.13)"><em>Dict</em></a><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a><em>]</em>) – The state dict to restore from</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.state_dict">
<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.state_dict" title="Permalink to this definition">¶</a></dt>
<dd><p>Objects should return their state_dict representation as a dictionary.
The output of this function will be checkpointed, and later restored in
<cite>load_state_dict()</cite>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Because of the inplace nature of restoring a checkpoint, this function
is also called during <cite>torch.distributed.checkpoint.load</cite>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The objects state dict</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>Dict</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<p>This <a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/torch/distributed/checkpoint/examples/fsdp_checkpoint_example.py">example</a> shows how to use Pytorch Distributed Checkpoint to save a FSDP model.</p>
<p>The following types define the IO interface used during checkpoint:</p>
<dl class="py class">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageReader</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader" title="Permalink to this definition">¶</a></dt>
<dd><p>Interface used by <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> to read from storage.</p>
<p>One StorageReader instance acts as both the coordinator and the follower
in a distributed checkpoint. As part of initialization, each instance
is told its role.</p>
<p>A subclass should expected the following sequence of calls by <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code>:</p>
<ol class="arabic simple" start="0">
<li><p>(all ranks) set checkpoint_id if users pass a valid checkpoint_id.</p></li>
<li><p>(all ranks) read_metadata()</p></li>
<li><p>(all ranks) set_up_storage_reader()</p></li>
<li><p>(all ranks) prepare_local_plan()</p></li>
<li><p>(coordinator) prepare_global_plan()</p></li>
<li><p>(all ranks) read_data()</p></li>
</ol>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_global_plan">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform centralized planning of storage loading.</p>
<p>This method is only called on the coordinator instance.</p>
<p>While this method can produce a completely different plan, the preferred
way is to store storage specific data in LoadPlan::storage_data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>plans</strong> (<a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.13)"><em>List</em></a><em>[</em><a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.planner.LoadPlan"><em>LoadPlan</em></a><em>]</em>) – A list of <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> instances, one for each rank.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A list of transformed <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> after storage global planning</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.List" title="(in Python v3.13)"><em>List</em></a>[<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.planner.LoadPlan"><em>LoadPlan</em></a>]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_local_plan">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform storage-specific local planning.</p>
<p>While this method can produce a completely different plan, the recommended
way is to store storage specific data in LoadPlan::storage_data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>plan</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.LoadPlan"><em>LoadPlan</em></a>) – The local plan from the <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> in use.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A transformed <code class="docutils literal notranslate"><span class="pre">LoadPlan</span></code> after storage local planning</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.planner.LoadPlan"><em>LoadPlan</em></a></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_data">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_data" title="Permalink to this definition">¶</a></dt>
<dd><p>Read all items from <code class="docutils literal notranslate"><span class="pre">plan</span></code> using <code class="docutils literal notranslate"><span class="pre">planner</span></code> to resolve the data.</p>
<p>A subclass should call <code class="docutils literal notranslate"><span class="pre">LoadPlanner::load_bytes</span></code> to deserialize a BytesIO
object into the right place.</p>
<p>A subclass should call <code class="docutils literal notranslate"><span class="pre">LoadPlanner::resolve_tensor</span></code> to get access to the
tensors that in should load data into.</p>
<p>It’s the StorageLayer responsibility to properly schedule any cross device copies
required.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>plan</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlan" title="torch.distributed.checkpoint.LoadPlan"><em>LoadPlan</em></a>) – The local plan to execute on</p></li>
<li><p><strong>planner</strong> (<a class="reference internal" href="#torch.distributed.checkpoint.LoadPlanner" title="torch.distributed.checkpoint.LoadPlanner"><em>LoadPlanner</em></a>) – The planner object to use to resolve items.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A future that completes once all reads are finished.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="futures.html#torch.futures.Future" title="torch.jit.Future"><em>Future</em></a>[None]</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_metadata">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_metadata" title="Permalink to this definition">¶</a></dt>
<dd><p>Read the checkpoint metadata.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>The metadata object associated with the checkpoint being loaded.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p><em>Metadata</em></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.reset">
<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.reset" title="Permalink to this definition">¶</a></dt>
<dd><p>Calls to indicates a brand new checkpoint read is going to happen.
A checkpoint_id may be present if users set the checkpoint_id for
this checkpoint read. The meaning of the checkpiont_id is
storage-dependent. It can be a path to a folder/file or a key for
a key-value storage.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>checkpoint_id</strong> (<em>Union</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><em>str</em></a><em>, </em><a class="reference external" href="https://docs.python.org/3/library/os.html#os.PathLike" title="(in Python v3.13)"><em>os.PathLike</em></a><em>, </em><em>None</em><em>]</em>) – The ID of this checkpoint instance. The meaning of the checkpoint_id
depends on the storage. It can be a path to a folder or to a file.
It can also be a key if the storage is more like a key-value store.
(Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p>